Unlocking massively parallel spectral proper orthogonal decompositions in the PySPOD package

DOI10.1016/J.CPC.2024.109246MaRDI QIDQ6557931

Oliver T. Schmidt, Marcin Rogowski, Gianmarco Mengaldo, Brandon C. Y. Yeung, Lisandro Dalcin, Romit Maulik, Matteo Parsani

Publication date: 18 June 2024

Published in: Computer Physics Communications (Search for Journal in Brave)

zbMATH Keywords

MPIdynamical systemsparalleldistributedmodal decompositionspectral proper orthogonal decompositionSPOD

Mathematics Subject Classification ID

Numerical analysis (65-XX) Computer science (68-XX)

Cites Work

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• High-order accurate entropy-stable discontinuous collocated Galerkin methods with the summation-by-parts property for compressible CFD frameworks: scalable SSDC algorithms and flow solver
• Neural-network learning of SPOD latent dynamics
• \textit{Nektar}++: enhancing the capability and application of high-fidelity spectral/\(hp\) element methods
• Dynamic mode decomposition of numerical and experimental data
• Frequency–time analysis, low-rank reconstruction and denoising of turbulent flows using SPOD
• Turbulence and the dynamics of coherent structures. I. Coherent structures
• Spectral analysis of jet turbulence
• Spectral proper orthogonal decomposition and its relationship to dynamic mode decomposition and resolvent analysis
• Wave Packets and Turbulent Jet Noise
• Optimizing noncontiguous accesses in MPI--IO

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